Session Fidle à distance (NEW !)

Faute de pouvoir organiser des sessions en présentiel,
nous vous proposons une version Fidle à distance :-)

- Voir ou revoir en ligne -

https://www.youtube.com/channel/UC4Sukzudhbwr6fs10cXrJsQ

- Prochain rendez-vous -

Jeudi 25 mars, 14h, Séquence 6 : Du réseau autoencodeur (AE) au Variational Autoencoder (VAE) ou comment parcourir l'imagination de nos réseaux.
Principes et architecture d'un Variational Autoencoder (VAE). Problématiques liées à la gestion de "gros" datasets. Projection gaussienne - Génération de données - Morphing dans l'espace latent. Programmation avancée avec Keras - Datasets clusterisés. Exemple proposé : Mise en œuvre d'un VAE, génération de données et morphing dans l'espace latent.
Durée : 2h - Les paramètres de diffusion seront précisés la veille dans le wiki

A propos de Fidle à distance
Voir le programme
Voir ou revoir les vidéos

About Fidle

This repository contains all the documents and links of the Fidle Training .
Fidle (for Formation Introduction au Deep Learning) is a 2-day training session
co-organized by the Formation Permanente CNRS and the Resinfo/SARI and DevLOG CNRS networks.

The objectives of this training are :

• Understanding the bases of Deep Learning neural networks
• Develop a first experience through simple and representative examples
• Understanding Tensorflow/Keras and Jupyter lab technologies
• Apprehend the academic computing environments Tier-2 or Tier-1 with powerfull GPU

For more information, you can contact Fidle team at :
Don't forget to look at the Wiki

Current Version : 2.0.22

Course materials

Course slides The course in pdf format (12 Mo)	Notebooks     Get a Zip or clone this repository      (40 Mo)	Datasets All the needed datasets (1.2 Go)

Have a look about How to get and install these notebooks and datasets.

Jupyter notebooks

Linear and logistic regression

• LINR1 - Linear regression with direct resolution
  Low-level implementation, using numpy, of a direct resolution for a linear regression
• GRAD1 - Linear regression with gradient descent
  Low level implementation of a solution by gradient descent. Basic and stochastic approach.
• POLR1 - Complexity Syndrome
  Illustration of the problem of complexity with the polynomial regression
• LOGR1 - Logistic regression
  Simple example of logistic regression with a sklearn solution

Perceptron Model 1957

• PER57 - Perceptron Model 1957
  Example of use of a Perceptron, with sklearn and IRIS dataset of 1936 !

Basic regression using DNN

• BHPD1 - Regression with a Dense Network (DNN)
  Simple example of a regression with the dataset Boston Housing Prices Dataset (BHPD)
• BHPD2 - Regression with a Dense Network (DNN) - Advanced code
  A more advanced implementation of the precedent example

Basic classification using a DNN

• MNIST1 - Simple classification with DNN
  An example of classification using a dense neural network for the famous MNIST dataset
• MNIST2 - Simple classification with CNN
  An example of classification using a convolutional neural network for the famous MNIST dataset

Images classification with Convolutional Neural Networks (CNN)

• GTSRB1 - Dataset analysis and preparation
  Episode 1 : Analysis of the GTSRB dataset and creation of an enhanced dataset
• GTSRB2 - First convolutions
  Episode 2 : First convolutions and first classification of our traffic signs
• GTSRB3 - Training monitoring
  Episode 3 : Monitoring, analysis and check points during a training session
• GTSRB4 - Data augmentation
  Episode 4 : Adding data by data augmentation when we lack it, to improve our results
• GTSRB5 - Full convolutions
  Episode 5 : A lot of models, a lot of datasets and a lot of results.
• GTSRB6 - Full convolutions as a batch
  Episode 6 : To compute bigger, use your notebook in batch mode
• GTSRB7 - Batch reports
  Episode 7 : Displaying our jobs report, and the winner is...
• GTSRB10 - OAR batch script submission
  Bash script for an OAR batch submission of an ipython code
• GTSRB11 - SLURM batch script
  Bash script for a Slurm batch submission of an ipython code

Sentiment analysis with word embedding

• IMDB1 - Sentiment analysis with hot-one encoding
  A basic example of sentiment analysis with sparse encoding, using a dataset from Internet Movie Database (IMDB)
• IMDB2 - Sentiment analysis with text embedding
  A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)
• IMDB3 - Reload and reuse a saved model
  Retrieving a saved model to perform a sentiment analysis (movie review)
• IMDB4 - Reload embedded vectors
  Retrieving embedded vectors from our trained model
• IMDB5 - Sentiment analysis with a RNN network
  Still the same problem, but with a network combining embedding and RNN

Time series with Recurrent Neural Network (RNN)

• LADYB1 - Prediction of a 2D trajectory via RNN
  Artificial dataset generation and prediction attempt via a recurrent network
• SYNOP1 - Preparation of data
  Episode 1 : Data analysis and preparation of a usuable meteorological dataset (SYNOP)
• SYNOP2 - First predictions at 3h
  Episode 2 : RNN training session for weather prediction attempt at 3h
• SYNOP3 - 12h predictions
  Episode 3: Attempt to predict in a more longer term

Unsupervised learning with an autoencoder neural network (AE)

• AE1 - Prepare a noisy MNIST dataset
  Episode 1: Preparation of a noisy MNIST dataset
• AE2 - Building and training an AE denoiser model
  Episode 1 : Construction of a denoising autoencoder and training of it with a noisy MNIST dataset.
• AE3 - Playing with our denoiser model
  Episode 2 : Using the previously trained autoencoder to denoise data
• AE4 - Denoiser and classifier model
  Episode 4 : Construction of a denoiser and classifier model
• AE5 - Advanced denoiser and classifier model
  Episode 5 : Construction of an advanced denoiser and classifier model

Generative network with Variational Autoencoder (VAE)

• VAE1 - First VAE, using functional API (MNIST dataset)
  Construction and training of a VAE, using functional APPI, with a latent space of small dimension.
• VAE2 - First VAE, using a subclass model (MNIST dataset)
  Construction and training of a VAE, using model subclass, with a latent space of small dimension.
• VAE3 - Analysis of the VAE's latent space of MNIST dataset
  Visualization and analysis of the VAE's latent space of the dataset MNIST
• VAE5 - Another game play : About the CelebA dataset
  Episode 1 : Presentation of the CelebA dataset and problems related to its size
• VAE6 - Generation of a clustered dataset
  Episode 2 : Analysis of the CelebA dataset and creation of an clustered and usable dataset
• VAE7 - Checking the clustered dataset
  Episode : 3 Clustered dataset verification and testing of our datagenerator
• VAE8 - Training session for our VAE
  Episode 4 : Training with our clustered datasets in notebook or batch mode
• VAE9 - Data generation from latent space
  Episode 5 : Exploring latent space to generate new data
• VAE10 - SLURM batch script
  Bash script for SLURM batch submission of VAE8 notebooks

Miscellaneous

• ACTF1 - Activation functions
  Some activation functions, with their derivatives.
• NP1 - A short introduction to Numpy
  Numpy is an essential tool for the Scientific Python.
• SCRATCH1 - Scratchbook
  A scratchbook for small examples
• TSB1 - Tensorboard with/from Jupyter
  4 ways to use Tensorboard from the Jupyter environment

Installation

Have a look about How to get and install these notebooks and datasets.

Licence

[en] Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0)
[Fr] Attribution - Pas d’Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International
See License.
See Disclaimer.